from typing import List, Tuple, Union, Dict
from .base import Task
from backprop.models import BaseModel, AutoModel
from transformers.optimization import Adafactor
from backprop.utils.datasets import TextToTextDataset
import requests
TASK = "summarisation"
DEFAULT_LOCAL_MODEL = "t5-base-qa-summary-emotion"
LOCAL_ALIASES = {
"english-base": "t5-base-qa-summary-emotion",
"english-small": "t5-small"
}
[docs]class Summarisation(Task):
"""
Task for summarisation.
Attributes:
model:
1. Model name
2. Model name on Backprop's summarisation endpoint
3. Model object that implements the summarisation task
local (optional): Run locally. Defaults to False
api_key (optional): Backprop API key for non-local inference
device (optional): Device to run inference on. Defaults to "cuda" if available.
"""
def __init__(self, model: Union[str, BaseModel] = None,
local: bool = False, api_key: str = None, device: str = None):
models = AutoModel.list_models(task=TASK)
super().__init__(model, local=local, api_key=api_key, device=device,
models=models, task=TASK,
default_local_model=DEFAULT_LOCAL_MODEL)
[docs] @staticmethod
def list_models(return_dict=False, display=False, limit=None):
"""
Returns the list of models that can be used and finetuned with this task.
Args:
return_dict: Default False. True if you want to return in dict form. Otherwise returns list form.
display: Default False. True if you want output printed directly (overrides return_dict, and returns nothing).
limit: Default None. Maximum number of models to return -- leave None to get all models.
"""
return AutoModel.list_models(task=TASK, return_dict=return_dict, display=display, limit=limit, aliases=LOCAL_ALIASES)
[docs] def __call__(self, text: Union[str, List[str]]):
"""Perform summarisation on input text.
Args:
text: string or list of strings to be summarised - keep each string below 500 words.
Returns:
Summary string or list of summary strings.
"""
task_input = {
"text": text
}
if self.local:
return self.model(task_input, task="summarisation")
else:
task_input["model"] = self.model
res = requests.post("https://api.backprop.co/summarisation", json=task_input,
headers={"x-api-key": self.api_key}).json()
if res.get("message"):
raise Exception(f"Failed to make API request: {res['message']}")
return res["summary"]
[docs] def step(self, batch, batch_idx):
"""
Performs a training step and returns loss.
Args:
batch: Batch output from the dataloader
batch_idx: Batch index.
"""
return self.model.training_step(batch)
[docs] def finetune(self, params, validation_split: Union[float, Tuple[List[int], List[int]]]=0.15,
max_input_length: int=512, max_output_length: int=128,
epochs: int=20, batch_size: int=None,
optimal_batch_size: int=None, early_stopping_epochs: int=1,
train_dataloader=None, val_dataloader=None, step=None,
configure_optimizers=None):
"""
Finetunes a generative model for summarisation.
Note:
input_text and output_text in params must have matching ordering (item 1 of input must match item 1 of output)
Args:
params: Dictionary of model inputs. Contains 'input_text' and 'output_text' keys, with values as lists of input/output data.
max_input_length: Maximum number of tokens (1 token ~ 1 word) in input. Anything higher will be truncated. Max 512.
max_output_length: Maximum number of tokens (1 token ~ 1 word) in output. Anything higher will be truncated. Max 512.
validation_split: Float between 0 and 1 that determines what percentage of the data to use for validation
epochs: Integer specifying how many training iterations to run
batch_size: Batch size when training. Leave as None to automatically determine batch size.
optimal_batch_size: Optimal batch size for the model being trained -- defaults to model settings.
early_stopping_epochs: Integer determining how many epochs will run before stopping without an improvement in validation loss
train_dataloader: Dataloader for providing training data when finetuning. Defaults to inbuilt dataloder.
val_dataloader: Dataloader for providing validation data when finetuning. Defaults to inbuilt dataloader.
step: Function determining how to call model for a training step. Defaults to step defined in this task class.
configure_optimizers: Function that sets up the optimizer for training. Defaults to optimizer defined in this task class.
Examples::
import backprop
summary = backprop.Summarisation()
# Provide training data for task
inp = ["This is a long news article about recent political happenings.", "This is an article about some recent scientific research."]
out = ["Short political summary.", "Short scientific summary."]
params = {"input_text": inp, "output_text": out}
# Finetune
summary.finetune(params)
"""
inputs = params["input_text"]
outputs = params["output_text"]
assert len(inputs) == len(outputs)
step = step or self.step
configure_optimizers = configure_optimizers or self.configure_optimizers
dataset_params = {
"input": inputs,
"output": outputs,
"max_input_length": max_input_length,
"max_output_length": max_output_length
}
print("Processing data...")
dataset = TextToTextDataset(dataset_params, task=TASK, process_batch=self.model.process_batch, length=len(inputs))
super().finetune(dataset=dataset, validation_split=validation_split,
epochs=epochs, batch_size=batch_size, optimal_batch_size=optimal_batch_size,
early_stopping_epochs=early_stopping_epochs,
train_dataloader=train_dataloader, val_dataloader=val_dataloader,
step=step, configure_optimizers=configure_optimizers)